Text Generation
MLX
Safetensors
English
small-language-model
from-scratch
conversational
apple-silicon
research
Instructions to use OpenSML/Solstice-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenSML/Solstice-1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OpenSML/Solstice-1") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use OpenSML/Solstice-1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OpenSML/Solstice-1"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpenSML/Solstice-1" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenSML/Solstice-1", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
File size: 2,527 Bytes
7bcd331 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | {
"combined_peak_lr": 1.5e-05,
"development_cases": 300,
"fifth_trial": "Choose original starting checkpoint using matched repair trials 1/2 dev scores; allocate 10%-40% per domain using positive dev gains from first four. No chaining of experimental checkpoints.",
"final_lr_fraction": 0.1,
"legacy_dev_cases": 320,
"legacy_test_cases": 704,
"maximum_attempts": 5,
"new_objective_weight": 0.4,
"peak_lr": 2e-05,
"practical_success": "At least 5pp new heldout content correctness gain over EACH baseline with 97.5% paired scenario-bootstrap intervals above zero; preserve strict instruction compliance, domains and general benchmarks within predeclared tolerances.",
"preservation": "Both starting models and previous protected checkpoints remain immutable. Keep the highest-dev-ranked new candidate even if final results do not establish an improvement. Remove other new trial artifacts only after all final assessments complete.",
"public_tolerances": {
"chat_answer_cases": 2,
"chat_repetition_cases": 1,
"chat_stop_cases": 1,
"choice_mean_pp": 0.25,
"choice_per_task_pp": 1,
"ifeval_loose_pp": 0.5,
"ifeval_strict_pp": 0
},
"replay_kl_weight": 0.25,
"replay_objective_weight": 0.6,
"reserved_test_cases": 1000,
"seed": 2026110915,
"selected_new_conversations": 4096,
"selected_replay_conversations": 4096,
"selection": "Highest dev key with preservation guards; one finalist is frozen before reserved test or new public benchmark evaluation.",
"trials": [
{
"mixture": {
"abstention": 0.4,
"dialogue": 0.2,
"grounded": 0.4
},
"name": "Extraction and appropriate abstention",
"parent": "attempt5"
},
{
"mixture": {
"abstention": 0.4,
"dialogue": 0.2,
"grounded": 0.4
},
"name": "Matched repair from parent2000",
"parent": "parent2000"
},
{
"mixture": {
"abstention": 0.1,
"constraints": 0.65,
"dialogue": 0.15,
"grounded": 0.1
},
"name": "Practical combined instructions",
"parent": "attempt5"
},
{
"mixture": {
"abstention": 0.1,
"dialogue": 0.15,
"grounded": 0.15,
"reasoning": 0.6
},
"name": "Short checked reasoning",
"parent": "attempt5"
},
{
"mixture": null,
"name": "Development-selected combination",
"parent": null
}
],
"updates_per_attempt": 256,
"warmup_fraction": 0.1
}
|